End-to-End Deep ROI Image Compression

Author:

AKUTSU Hiroaki1,NARUKO Takahiro1

Affiliation:

1. R&D Group, Hitachi, Ltd.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference13 articles.

1. [1] H. Akutsu and T. Naruko, “End-to-end learned roi image compression,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.

2. [2] ITU, “Harnessing the internet of things for global development,” 2016.

3. [3] IDC, “Data age 2025.” https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf, 2018.

4. [4] H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection and classification using deep neural networks with smartphone images,” Computer-Aided Civil and Infrastructure Engineering, vol.33, no.12, pp.1127-1141, 2018. 10.1111/mice.12387

5. [5] M. Li, W. Zuo, S. Gu, D. Zhao, and D. Zhang, “Learning convolutional networks for content-weighted image compression,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.3214-3223, June 2018. 10.1109/cvpr.2018.00339

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